Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from pysentimiento.preprocessing import preprocess_tweet
+
+pl = pipeline("ner", tokenizer="google/electra-base-discriminator", model="Recognai/veganuary_ner", aggregation_strategy="first")
+
+def ner(text):
+    text = preprocess_tweet(text)
+    predictions = pl(text)    
+    return "\n".join([pred["word"] for pred in predictions if pred["entity_group"] == "LABEL_1"])
+
+iface = gr.Interface(
+    ner,
+    gr.inputs.Textbox(placeholder="copy&paste your veganuary tweet here ...", label="Tweet"),
+    gr.outputs.Textbox(label="List of detected food mentions in the tweet"),
+    examples=[
+        ["Fruit is delicious AND healthy. Brighten up your plate & palate with fresh watermelon, Greek yoghurt & berries, smashed avocado or lime added to water.  A piece of #fruit a day keeps the doctor away! #Veganuary2022 #vegetarian #plantbased #ActiveHealthMatters #PerspicuousHealth https://t.co/BvLUoVnPCh"]
+    ],
+    allow_flagging=False,
+    title="Veganuary NER",
+    description="Extract food entities from veganuary tweets 😋",
+)
+
+iface.launch(share=False)